Soft information in online peer-to-peer lending : Evidence from a leading platform in China

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review

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Original languageEnglish
Article number100873
Journal / PublicationElectronic Commerce Research and Applications
Online published28 Jun 2019
Publication statusPublished - Jul 2019


We mainly investigate the relation of soft factors and their valid verification to the probability of listings being fully funded and to the default probability of loans, as well as the relations between soft factors and the listing items in the listing issuing and funding processes in peer-to-peer lending market. Using data collected from the most popular lending platform in China, we find that most soft factors predict the probability of a listing becoming successfully funded as well as the default probability of a loan. Specifically, borrowers who are older, married, and have a higher educational background are more welcomed among lenders. Borrowers who possess cars and houses, have higher monthly income, and write more words in the textual descriptions of their listings are more likely to get their listings fully funded. At the same time, the valid verification of some soft factors can predict the probability of a listing being funded, but fail in predicting the loans’ default probability. Moreover, there are some interesting relationships between the soft factors and listing items in the listing issuing and bidding processes. The older married borrowers are more inclined to issue listings at lower interest rates, but actually obtain loans at the cost of paying higher interest rates. Borrowers with better profiles, including those who are married, have higher educational backgrounds and higher income levels, and living in first-tier cities tend to issue listings with shorter terms, larger request amounts, and lower interest rates. Furthermore, we find that the lenders have correctly identified signals from the borrowers’ higher educational backgrounds and status of possessing cars and houses, but wrongly recognized the signals from the borrowers’ higher income and the length of loan descriptions. Generally, most of the borrowers’ soft information helps to judge their creditworthiness for the platform as well as for lenders.

Research Area(s)

  • Asymmetry information, Peer-to-peer lending,, Soft information

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